Feasibility of Randomized Controlled Trials for Cancer Drugs Approved by the Food and Drug Administration Based on Single-Arm Studies
Bibliographic record
Abstract
Background: The US Food and Drug Administration (FDA) introduced an Accelerated Approval (AA) pathway to expedite patient access to new drugs. AA accepts less rigorous trial designs, including single-arm studies (SAS), owing to perceived lack of feasibility of timely randomized controlled trials (RCTs). Methods: We designed hypothetical RCTs with endpoints of overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) for FDA approvals based on SAS for solid tumors during 2010-2019. Existing standards of care served as controls. RCTs were designed to detect a difference with power of 0.80, α-error of 5% (2-sided), and 1:1 randomization. Accrual duration was estimated based on participation by less than 5% of eligible patients derived from cancer-specific incidence and mortality rates in the United States. Results: Of 172 (18.0%) approvals during the study period, 31 (18.0%) were based on SAS. Median sample size was 104 (range = 23-411), and 77.4% were AA. All studies reported ORR, 55% reported duration of response, 19.4% reported PFS, and 22.5% reported OS. Median sample sizes needed to conduct RCTs with endpoints of ORR, PFS, and OS were 206, 130, and 396, respectively. It would have been theoretically possible to conduct RCTs within duration comparable with that required by SAS for 84.6%, 94.1%, and 80.0% of approvals with endpoints of ORR, PFS, and OS, respectively. Conclusion: An overwhelming majority of FDA approvals based on SAS should be feasible as RCTs within a reasonable time frame. Given the collateral harms to patients and to scientific rigor, drug approval based on SAS should only be permitted under exceptional circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.742 | 0.791 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".